Exploring Drug-Drug Interactions: A Network Analysis and Visualization Approach

Abstract

This article investigates the complexity of drug-drug interactions through network analysis and visualization. A network-based approach is presented to analyze drug-drug interactions and provide an interactive visualization tool by exploring relationships between drugs. The network-based approach is applied to a large drug-drug interaction dataset and the properties of the resulting network are analyzed. The potential of the network-based approach for further exploration of drug-drug interactions is also discussed. Finally, the effectiveness of the network-based approach is demonstrated by providing an interactive visualization tool to discover relationships between drugs. The results of this study are expected to facilitate a better understanding of the complexity of drug-drug interactions and suggest potential applications of network analysis and visualization in drug discovery and development. It has also published Pyvis network graphs online at https://iuysal1905-streamlit-pyvis-network-app2-91q9sv.streamlit.app so that users can visit the web application and interact with the graphs directly.

Keywords

• Drug-drug interaction
• Network analysis
• Visualization.

İlaç-İlaç Etkileşimlerini Keşfetmek: Bir Ağ Analizi ve Görselleştirme Yaklaşımı

Öz

Bu makale, ağ analizi ve görselleştirme yoluyla ilaç-ilaç etkileşimlerinin karmaşıklığını araştırmaktadır. İlaç-ilaç etkileşimlerini analiz etmek ve ilaçlar arasındaki ilişkileri keşfederek etkileşimli bir görselleştirme aracı sağlamak için ağ tabanlı bir yaklaşım sunulmaktadır. Ağ tabanlı yaklaşım, büyük bir ilaç-ilaç etkileşimi veri kümesine uygulanmakta ve ortaya çıkan ağın özelliklerini analiz etmektedir. Ayrıca, ilaç-ilaç etkileşimlerinin daha fazla araştırılması için ağ tabanlı yaklaşımın potansiyeli de tartışılmaktadır. Son olarak, ilaçlar arasındaki ilişkileri keşfetmek için etkileşimli bir görselleştirme aracı sağlayarak ağ tabanlı yaklaşımın etkinliği gösterilmektedir. Bu çalışmanın sonuçları, ilaç-ilaç etkileşimlerinin karmaşıklığının daha iyi anlaşılmasını sağlayacağı öngörülmekte ve ilaç keşfi ve geliştirmede ağ analizi ve görselleştirmenin potansiyel uygulamalarını önermektedir. Aynı zamanda kullanıcıların web uygulamasını ziyaret edebilmeleri ve grafiklerle doğrudan etkileşim kurabilmeleri için Pyvis ağ grafiklerini çevrimiçi olarak https://iuysal1905-streamlit-pyvis-network-app2-91q9sv.streamlit. app adresinde yayınlanmıştır.

Anahtar Kelimeler

• İlaç-ilaç etkileşimi
• Ağ analizi
• Görselleştirme.

Kaynakça

1. Al-Rabeah M. H., Lakizadeh A. Prediction of drug-drug interaction events using graph neural networks based feature extraction. Scientific Reports, 12(1), 15590, 2022.
2. Annamalai S., Shin W. S. Efficient degradation of trimethoprim with ball-milled nitrogen-doped biochar catalyst via persulfate activation. Chemical Engineering Journal, 440, 135815, 2022.
3. Aric A. Hagberg, Daniel A. Schult and Pieter J. Swart, “Exploring network structure, dynamics, and function using NetworkX”, in Proceedings of the 7th Python in Science Conference (SciPy2008), Gäel Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pp. 11–15, 2008.
4. Azuaje F. Drug interaction networks: an introduction to translational and clinical applications. Cardiovascular research, 97(4), 631-641, 2013.
5. Brunet M., Pastor-Anglada M. Insights into the Pharmacogenetics of Tacrolimus Pharmacokinetics and Pharmacodynamics. Pharmaceutics, 14(9), 1755, 2022.
6. Büyükokuroğlu M. E., Tanyeri P., Keleş R. İlaç-ilaç etkileşimleri konusunda farkındalık. Online Türk Sağlık Bilimleri Dergisi, 4(3), 377-391, 2019.
7. Correia R. B., Li L., Rocha L. M. Monitoring potential drug interactions and reactions via network analysis of instagram user timelines. In Biocomputing 2016: Proceedings of the Pacific Symposium, 492-503, 2016.
8. Emadi A., Gore S. D. Arsenic trioxide—an old drug rediscovered. Blood reviews, 24(4-5), 191-199, 2010.

9. Ezeh M. I., Okonkwo O. E., Okpoli I. N., Orji C. E., Modozie B. U., Onyema A. C., Ezebuo F. C. Chemoinformatic Design and Profiling of Derivatives of Dasabuvir, Efavirenz, and Tipranavir as Potential Inhibitors of Zika Virus RNA-Dependent RNA Polymerase and Methyltransferase. ACS omega, 7(37), 33330-33348, 20222.
10. Faré P. B., Memoli E., Treglia G., Bianchetti M. G., Milani G. P., Marchisio P., Janett S. Trimethoprim-associated hyperkalaemia: a systematic review and meta-analysis. Journal of Antimicrobial Chemotherapy, 77(10), 2588-2595, 2022.
11. Farley J. H., Brady W. E., O'Malley D., Fujiwara K., Yonemori K., Bonebrake A., Gershenson D. M. A phase II evaluation of temsirolimus with carboplatin and paclitaxel followed by temsirolimus consolidation in clear cell ovarian cancer: An NRG oncology trial. Gynecologic Oncology, 167(3), 423-428, 2022.
12. Hauben M. Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug–Drug Interactions. Clinical Therapeutics, 2023.
13. Jiang M., Yang F., Zhang L., Xu D., Jia Y., Cheng Y., Xing Q. Unique motif shared by HLA‐B* 59: 01 and HLA‐B* 55: 02 is associated with methazolamide‐induced Stevens–Johnson syndrome and toxic epidermal necrolysis in Han Chinese. Journal of the European Academy of Dermatology and Venereology, 36(6), 873-880, 2022.
14. Juhi A., Pipil N., Santra S., Mondal S., Behera J. K., Mondal H., Behera IV, J. K. The capability of ChatGPT in predicting and explaining common drug-drug interactions. Cureus, 15(3), 2023.
15. Kim D. K., Han D., Bae J., Kim H., Lee S., Kim J. S., Park H. W. Verapamil-loaded supramolecular hydrogel patch attenuates metabolic dysfunction-associated fatty liver disease via restoration of autophagic clearance of aggregated proteins and inhibition of NLRP3. Biomaterials Research, 27(1), 1-21, 2023.
16. Korkmaz S., Yıldız S., Demir C. F., Sünbül Z. E., Korucu T., Gündoğan B. Aripiprazole Bağlı Akut Distonik Reaksiyon. Fırat Üniversitesi Sağlık Bilimleri Tıp Dergisi, 29(2), 91-92, 2015.
17. Kotzeva A., Mittal D., Desai S., Judge D., Samanta K. Socioeconomic burden of schizophrenia: a targeted literature review of types of costs and associated drivers across 10 countries. Journal of medical economics, 26(1), 70-83, 2023.
18. Kumar D. A., Jalaluddin D. How To Manage Dental Anxiety And Fear Among Paediatric Patients. International Journal of Current Science (IJCSPUB). Volume 12, Issue 4 December 2022 | ISSN: 2250-1770, 2022.
19. Ladd J., Otis J., Warren C. N., Weingart S. Exploring and analyzing network data with Python. Programming Historian, 6, 2017.
20. Leung K. Network Analysis and Visualization of Drug-Drug Interactions. 2021, 8 Mart 2023 tarihinde https://towardsdatascience.com/network-analysis-and-visualization-of-drug-drug-interactions-1e0b41d0d3df adresinden erişildi.
21. Lin X., Quan Z., Wang Z. J., Ma T., Zeng X. KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction. In IJCAI Vol. 380, 2739-2745, 2020.
22. Matveychuk D., MacKenzie E. M., Kumpula D., Song M. S., Holt A., Kar, S., Baker G. B. Overview of the neuroprotective effects of the MAO-inhibiting antidepressant phenelzine. Cellular and Molecular Neurobiology, 1-18, 2022.
23. Niu J., Straubinger R. M., Mager D. E. Pharmacodynamic drug–drug interactions. Clinical Pharmacology & Therapeutics, 105(6), 1395-1406, 2019.
24. Nwabuife J. C., Omolo C. A., Govender T. Nano delivery systems to the rescue of ciprofloxacin against resistant bacteria “E. coli; P. aeruginosa; Saureus; and MRSA” and their infections. Journal of Controlled Release, 349, 338-353, 2022.
25. Pruette M. E., Zarzar T. R., Sheitman B. B. Expanding clozapine use in state prisons: a review of the North Carolina experience. Journal of correctional health care, 2023.
26. Qin X., Xie C., Hakenjos J. M., MacKenzie K. R., Boyd S. R., Barzi M., Li F. The roles of Cyp1a2 and Cyp2d in pharmacokinetic profiles of serotonin and norepinephrine reuptake inhibitor duloxetine and its metabolites in mice. European Journal of Pharmaceutical Sciences, 181, 106358, 2023.
27. Shrestha N., Banga A. K. Development and evaluation of transdermal delivery system of tranylcypromine for the treatment of depression. Drug Delivery and Translational Research, 1-11, 2022.
28. Swapna G., Pravallika B., Poojitha J. A Review on Drug-drug interaction studies on Amiodarone and Levofloxacin. Research journal of Pharmacology and Pharmacodynamics, 11(4), 147-152, 2019.
29. Tedesco-Silva H., Saliba F., Barten M. J., De Simone P., Potena L., Gottlieb J., Pascual J. An overview of the efficacy and safety of everolimus in adult solid organ transplant recipients. Transplantation Reviews, 36(1), 100655, 2022.
30. Van Haarst A., Smith S., Garvin C., Benrimoh N., Paglialunga S. Rifampin drug–drug–interaction studies: reflections on the nitrosamine impurities issue. Clinical Pharmacology - Therapeutics, 113(4), 816-821, 2023.
31. Vo T. H., Nguyen N. T. K., Kha Q. H., Le N. Q. K. On the road to explainable AI in drug-drug interactions prediction: A systematic review. Computational and Structural Biotechnology Journal, 2022.
32. Yakut K., Erdoğan İ., Daldaban B. Amiodarona Bağlı Nadir Görülen Bir Komplikasyon; Şiddetli Karın Ağrısı. Türkiye Çocuk Hastalıkları Dergisi, 11(1), 69-71, 2017.
33. Yu Hui. “Data of multiple-type drug-drug interactions”, Mendeley Data, V1, doi: 10.17632/md5czfsfnd.1, 2020.
34. Zagidullin B., Aldahdooh J., Zheng S., Wang W., Wang Y., Saad J., Tang J. DrugComb: an integrative cancer drug combination data portal. Nucleic acids research, 47(W1), W43-W51, 2019.
35. Zhu W., Barreto E. F., Li J., Lee H. K., Kashani K. Drug-drug interaction and acute kidney injury development: A correlation-based network analysis. Plos one, 18(1), e0279928, 2023.
36. Zuccato C., Cosenza L. C., Zurlo M., Gasparello J., Papi C., D’Aversa E., Gambari R. Expression of γ-globin genes in β-thalassemia patients treated with sirolimus: results from a pilot clinical trial (Sirthalaclin). Therapeutic Advances in Hematology, 13, 20406207221100648, 2022.